{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visuals for NFL Receiver Clusters\n",
    "- 2018 regular season statistics for all players targeted at least once.\n",
    "- Data sourced from [Pro-Football-Reference](https://www.pro-football-reference.com/years/2018/receiving.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\n",
    "import yellowbrick.datasets as ybdata\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nfl_df = (ybdata.load_nfl(return_dataset=True)\n",
    "                .to_dataframe()\n",
    "                .query('Tgt >= 20'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Id</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>Tgt</th>\n",
       "      <th>Rec</th>\n",
       "      <th>Ctch_Rate</th>\n",
       "      <th>...</th>\n",
       "      <th>FirstTeamAllPro</th>\n",
       "      <th>C_pos</th>\n",
       "      <th>CB_pos</th>\n",
       "      <th>DT_pos</th>\n",
       "      <th>FB_pos</th>\n",
       "      <th>QB_pos</th>\n",
       "      <th>RB_pos</th>\n",
       "      <th>T_pos</th>\n",
       "      <th>TE_pos</th>\n",
       "      <th>WR_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Michael Thomas</td>\n",
       "      <td>ThomMi05</td>\n",
       "      <td>NOR</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>147</td>\n",
       "      <td>125</td>\n",
       "      <td>0.850</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Zach Ertz</td>\n",
       "      <td>ErtzZa00</td>\n",
       "      <td>PHI</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>156</td>\n",
       "      <td>116</td>\n",
       "      <td>0.744</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DeAndre Hopkins</td>\n",
       "      <td>HopkDe00</td>\n",
       "      <td>HOU</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>163</td>\n",
       "      <td>115</td>\n",
       "      <td>0.706</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Julio Jones</td>\n",
       "      <td>JoneJu02</td>\n",
       "      <td>ATL</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>170</td>\n",
       "      <td>113</td>\n",
       "      <td>0.665</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Adam Thielen</td>\n",
       "      <td>ThieAd00</td>\n",
       "      <td>MIN</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>153</td>\n",
       "      <td>113</td>\n",
       "      <td>0.739</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk           Player        Id   Tm  Age   G  GS  Tgt  Rec  Ctch_Rate  \\\n",
       "0   1   Michael Thomas  ThomMi05  NOR   25  16  16  147  125      0.850   \n",
       "1   2        Zach Ertz  ErtzZa00  PHI   28  16  16  156  116      0.744   \n",
       "2   3  DeAndre Hopkins  HopkDe00  HOU   26  16  16  163  115      0.706   \n",
       "3   4      Julio Jones  JoneJu02  ATL   29  16  16  170  113      0.665   \n",
       "4   5     Adam Thielen  ThieAd00  MIN   28  16  16  153  113      0.739   \n",
       "\n",
       "    ...    FirstTeamAllPro  C_pos  CB_pos  DT_pos  FB_pos  QB_pos  RB_pos  \\\n",
       "0   ...                  1      0       0       0       0       0       0   \n",
       "1   ...                  0      0       0       0       0       0       0   \n",
       "2   ...                  1      0       0       0       0       0       0   \n",
       "3   ...                  0      0       0       0       0       0       0   \n",
       "4   ...                  0      0       0       0       0       0       0   \n",
       "\n",
       "   T_pos  TE_pos  WR_pos  \n",
       "0      0       0       1  \n",
       "1      0       1       0  \n",
       "2      0       0       1  \n",
       "3      0       0       1  \n",
       "4      0       0       1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Rec', 'Yds', 'TD', 'Fmb', 'Ctch_Rate']\n",
    "X = nfl_df[features].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "elbow_visualizer = KElbowVisualizer(KMeans(random_state=42), k=(2,10), timings=False)\n",
    "elbow_visualizer.fit(X)\n",
    "elbow_visualizer.size = (600, 400)\n",
    "elbow_visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = KMeans(4, random_state=42)\n",
    "silhouette_visualizer = SilhouetteVisualizer(model, colors='yellowbrick')\n",
    "\n",
    "silhouette_visualizer.fit(X)\n",
    "elbow_visualizer.size = (600, 400)\n",
    "silhouette_visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
